This is a meaty prompt and I burned a few minutes just decomposing it.
Start by clarifying requirements and scale, then design the core data model and APIs for auctions and bids. Focus on real-time bid updates and strict bid ordering, using a pub/sub system and atomic operations. Discuss trade-offs between consistency and availability, and how to handle auction end and winner determination.
Pro tip: Emphasize idempotency and race condition handling in bid placement, as multiple users may bid simultaneously. Also, discuss how to efficiently broadcast bid updates to thousands of watchers without overwhelming the system.
Ask about expected number of concurrent auctions, users, and bids per second. Clarify consistency requirements (e.g., strict bid ordering) and latency expectations for real-time updates.
Define schemas for auctions and bids, ensuring bids are strictly increasing. Design REST or WebSocket APIs for browsing, bidding, and subscribing to updates.
Use a centralized service or distributed lock to serialize bids per auction, or employ optimistic concurrency control with versioning. Validate that each new bid is higher than the current highest.
Use a pub/sub system (e.g., Redis Pub/Sub, Kafka) to broadcast bid updates to subscribers. Consider WebSockets for pushing updates to clients, and handle reconnection and missed updates.
Schedule a job at auction end time to close bidding and determine the winner. Ensure no bids are accepted after the end time, and handle clock skew across servers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Walked through auth check first, then monotonic amount validation, then persist, then publish to a message queue.
Start by clarifying the requirements: bid volume, latency, consistency needs, and failure scenarios. Then propose a pipeline that decouples validation, persistence, and publication using a durable log (e.g., Kafka) to ensure ordering and no data loss. Finally, discuss trade-offs and how you would handle failures, idempotency, and monitoring.
Pro tip: Emphasize idempotency and exactly-once semantics—interviewers at Meta care about correctness under failures, not just happy-path design. Mention that you'd use a transactional outbox or change data capture to avoid dual-write inconsistencies.
Ask about expected bid volume, latency requirements, consistency guarantees, and failure tolerance. This shows you don't jump to solutions without understanding the problem.
Propose a three-stage pipeline: validation (syntactic and semantic), persistence (durable storage), and publication (to downstream consumers). Explain how each stage can be scaled independently.
Use a durable, partitioned log (e.g., Kafka) with keys to maintain per-bid ordering. Implement idempotent producers and consumers, and consider transactional writes to guarantee exactly-once processing.
Describe how to handle validation failures (dead-letter queue), persistence failures (retries with backoff), and publication failures (outbox pattern). Ensure that retries don't cause duplicates.
Discuss monitoring (lag, error rates, throughput) and alerting. Mention how you'd evolve the system as volume grows, e.g., sharding or adding more partitions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: strict per-auction ordering and high throughput. Then propose partitioning the queue by auction ID (or a hash of it) so all messages for a given auction go to the same partition, processed by a single consumer in order. Finally, discuss how to scale consumers across partitions and handle hot auctions.
Pro tip: Mention that you can use a composite key like auction ID plus a sequence number to detect out-of-order messages, and consider using Kafka's partitioner with a custom strategy to avoid hot partitions.
Confirm that ordering is required only per auction, not globally, and that the system must handle high throughput and scale horizontally. Ask about expected message volume and latency requirements.
Use auction ID as the partition key so all messages for a given auction are routed to the same partition. This ensures per-auction ordering because a single consumer processes each partition sequentially.
Assign each partition to exactly one consumer within a consumer group. Scale by adding more partitions and consumers, but ensure that the number of consumers does not exceed partitions to avoid idle consumers.
If some auctions are extremely active, they may overload a single partition. Mitigate by splitting hot auctions into sub-partitions (e.g., auction ID + shard) and merging results downstream, or by using a dedicated queue for hot auctions.
Discuss how consumer failures trigger rebalancing, which may temporarily pause processing. Ensure that offsets are committed only after processing to avoid message loss, and consider idempotent processing to handle duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
10k concurrent auctions, assumed maybe 5 bids per minute per active auction in peak periods, so roughly 800-900 bid writes per second.
Start by estimating the daily active users (DAU) and the number of auctions they participate in, then derive the average QPS for core actions like bidding and viewing. Next, apply a fan-out multiplier to account for the number of viewers per auction, and sum the QPS across all actions to get the total system load.
Pro tip: Always state your assumptions clearly and round numbers to powers of 10 for simplicity; interviewers care more about your reasoning process than exact figures.
Start with Meta's scale: estimate DAU (e.g., 2 billion) and the fraction interested in auctions (e.g., 1% = 20 million). Then estimate how many auctions each user views or participates in per day.
For each core action (e.g., viewing an auction, placing a bid), compute the average QPS by dividing daily actions by 86,400 seconds. Use peak-to-average ratio (e.g., 2-3x) to estimate peak QPS.
Determine the average number of viewers per auction (e.g., 100). Multiply the number of concurrent auctions by viewers to get total concurrent viewers, then estimate QPS for view-related actions (e.g., fetching auction details, live updates).
Add QPS from all actions (bidding, viewing, notifications) to get total system QPS. Sanity-check by comparing to known Meta service scales (e.g., billions of QPS for large systems).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Single writer per auctionId for the end handler, combined with an idempotent close operation.
Start by clarifying the auction semantics and constraints, then propose a design that uses a single source of truth with atomic operations to determine the winner. Explain how you handle race conditions and retries using idempotency, versioning, and distributed locking or consensus, and discuss trade-offs between consistency and availability.
Pro tip: Emphasize that the winner determination should be idempotent and based on a deterministic rule (e.g., highest bid, earliest timestamp) so that retries don't change the outcome. Also, mention that you would use a conditional write (like compare-and-swap) to atomically set the winner, ensuring exactly-once semantics.
Ask about auction rules (e.g., highest bid wins, tie-breaking), expected scale, consistency requirements, and whether the system is distributed. This ensures your solution addresses the right problem.
Propose using a centralized database or a distributed consensus system (like etcd or ZooKeeper) to store auction state and bids. This avoids split-brain and ensures a consistent view.
Use atomic operations (e.g., conditional writes, transactions, or compare-and-swap) to select the winner exactly once. For example, a database transaction that checks the auction is still open and updates the winner in one atomic step.
Make the winner determination idempotent by using a unique auction ID and a deterministic winner selection. If a retry occurs, the system should recognize the winner is already set and return the same result without side effects.
Explain how you handle network partitions, node failures, and latency. Discuss trade-offs between strong consistency (e.g., using consensus) and availability, and how you might use optimistic concurrency or locks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sniping mitigation I'd heard of before: extend the auction by a short window (like 2 minutes) if a bid comes in within the last 30 seconds.
Start by clarifying the system context (e.g., auction, marketplace, or ad bidding) and define fraud and sniping as distinct problems with different motivations. Then propose layered mitigations that balance security, user experience, and system performance, explaining trade-offs and how you'd measure success.
Pro tip: Acknowledge that some sniping is legitimate user behavior, so mitigations should avoid penalizing normal users; focus on detecting and blocking automated or fraudulent patterns instead.
Ask questions to understand the system, business model, and what constitutes fraud vs. legitimate sniping. Identify key metrics like false positive rate, latency, and user impact.
Enumerate common fraud tactics (e.g., fake accounts, bid shielding) and sniping behaviors (e.g., last-millisecond bids, bots). Consider both automated and manual attacks.
Suggest a combination of preventive, detective, and reactive measures. Examples: rate limiting, CAPTCHA, behavioral analysis, ML-based anomaly detection, and auction extensions.
Discuss how each mitigation affects latency, scalability, user experience, and cost. Prioritize based on risk and business impact.
Outline metrics to track (e.g., fraud rate, sniping incidents, false positives) and a plan to continuously improve models and rules.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.